3. Scatter函数

使用go.Scatter()函数可以绘制散点图、气泡图和线形图，常用参数如下：

• hoverinfo：当用户与图形互动时，鼠标指针显示的参数。
• line：线条参数，包括设置线条宽度width、颜色color、虚线类型dash、插值方法shape等。
• marker：数据节点参数，包括设置大小size、颜色color、颜色模式colorscale、显示颜色条showscale、透明度opacity等。
• mode：图形格式参数，包括lines、markers，使用+号进行模式组合，比如lines+markers图形。超过20个points，默认为lines。
• name：图形名称参数。
• opacity：透明度参数，范围是0-1。
• showlengend：布尔变量，用于设置图例显示的参数。
• text：文本参数。
• xaxis\yaxis：x\y的坐标轴参数。
• x\y：x\y轴的数据参数。

3.1 散点图 Scatter

# 3-1 Line and Scatter Plots 多类型散点图
import plotly.graph_objects as go
import numpy as np

N = 100
x = np.linspace(0, 1, N)  # linspace()函数返回N个0-1均匀分布的样本
y0 = np.random.randn(N) + 5
y1 = np.random.randn(N)  # random.randn()函数返回N个服从标准正态分布（均值0方差1）的随机样本
y2 = np.random.randn(N) - 5

fig = go.Figure()

# 添加traces
fig.add_trace(go.Scatter(x=x, y=y0,
                         mode='markers',  # 纯散点的绘图
                         name='markers'))
fig.add_trace(go.Scatter(x=x, y=y1,
                         mode='lines+markers',  # 散点+线的绘图
                         name='lines+markers'))
fig.add_trace(go.Scatter(x=x, y=y2,
                         mode='lines',  # 线的绘图
                         name='lines'))

fig.show()

# 3-2 Styled Scatter Plots 风格化散点图
import plotly.graph_objects as go
import numpy as np

N = 100
x = np.linspace(0, 1, N)  # linspace()函数返回N个0-1均匀分布的样本
y0 = np.random.randn(N) + 5
y1 = np.random.randn(N)  # random.randn()函数返回N个服从标准正态分布（均值0方差1）的随机样本
y2 = np.random.randn(N) - 5

fig = go.Figure()

# 添加traces
fig.add_trace(go.Scatter(x=x, y=y0,
                         mode='markers',  # 纯散点的绘图
                         name='markers',
                         marker=dict(
                             size=10,  # 设置点的大小
                             color='#f79337'  # 设置点的颜色
                         )))
fig.add_trace(go.Scatter(x=x, y=y1,
                         mode='lines+markers',  # 散点+线的绘图
                         name='lines+markers',
                         marker=dict(
                             size=10, 
                             color='#E15759', 
                             line=dict(width=1, color='rgba(0,0,0,0.2)') # 设置点的描边大小和颜色
                        )))
fig.add_trace(go.Scatter(x=x, y=y2,
                         mode='lines',  # 线的绘图
                         name='lines',
                         line=dict(
                             width=2,  # 设置线条的宽度
                             color='#76B7B2'  # 设置线条的颜色
                         )))

fig.show()

3.2 气泡图 Bubble

# 3-3 Simple Bubble Chart 气泡图_设置气泡大小
import plotly.graph_objects as go
import numpy as np

N = 100
x = np.linspace(0,1,N)
y = np.random.randn(N)
z = np.random.randint(50,size=N) # random.randint()函数返回N个50以内的整数

fig = go.Figure(data=go.Scatter(
    x=x,y=y,
    mode='markers',
    marker=dict(size=z),  # 设置气泡的大小，可当作第三个维度
))

fig.show()

# 延伸：缩放气泡大小可以使用属性sizeref，建议使用以下公式来计算sizeref值：
# sizeref =2.* max（size数组）/（所需的标记 ** 2）
import plotly.graph_objects as go
import numpy as np

N = 100
x = np.linspace(0,1,N)
y = np.random.randn(N)
z = np.random.randint(500,size=N) # random.randint()函数返回N个500以内的整数

fig = go.Figure(data=go.Scatter(
    x=x,y=y,
    mode='markers',
    marker=dict(
        size=z,
#         sizemode='are',
        sizeref=2.*max(z)/(10**2)  # 使用属性sizeref缩放气泡大小
    )))

fig.show()

# 3-4 Simple Bubble Chart 气泡图_设置气泡颜色
import plotly.graph_objects as go
import numpy as np

N = 100
x = np.linspace(0,1,N)
y = np.random.randn(N)
z = np.random.randint(50,size=N) # random.randint()函数返回N个50以内的整数

fig = go.Figure(data=go.Scatter(
    x=x,y=y,
    mode='markers',
    text=z,  # 设置显示的文本
    marker=dict(size=z,  # 固定气泡大小
                color=z,  # 设置气泡的颜色，可当作第三个维度
                colorscale='Viridis', # 设置colorscale（颜色范围）
                showscale=True,  # 设置是否显示scale（右侧的颜色条）
                opacity=0.6  # 设置气泡的不透明度
               )))

fig.show()

# 3-5 Simple Bubble Chart 气泡图_自定义文本标签
import plotly.graph_objects as go
import numpy as np

N = 100
x = np.linspace(0,1,N)
y = np.random.randn(N)
z = np.random.randint(50,size=N) # random.randint()函数返回N个50以内的整数

text=[]
for i in range(N):
    text.append(('x={0}<br>y={1}<br>size={2}').format(np.round(x[i],2),np.round(y[i],2),z[i]))

fig = go.Figure(data=go.Scatter(
    x=x,y=y,
    mode='markers',
    text=text,
    hoverinfo='text',
    marker=dict(size=z,  # 设置气泡的大小，可当作第三个维度
                color=z,  # 设置气泡的颜色，可当作第三个维度
                colorscale='Viridis', # 设置colorscale（颜色范围）
                showscale=True,  # 设置是否显示scale（右侧的颜色条）
                opacity=0.6  # 设置气泡的不透明度
               )))

fig.show()

练习1. 绘制散点图 Sales and Profit Distrubution

导入数据文件'Sample - Superstore.xls'，绘制散点图，展示商品子类别（Sub-Category）中'Paper'销售额（Sales）和利润（Profit）的相关关系，用气泡的颜色来展示Discount的取值大小，从而进一步分析这些变量之间的关系。

# Step1. 导入数据：导入文件'Sample - Superstore.xls'，并查看数据
import pandas as pd
df = pd.read_excel('Sample - Superstore.xls',sheet_name='Orders')
df.shape

(9994, 21)

# Step2. 数据筛选：对'Sub-Category'中'Paper'产品进行筛选
data = df[df['Sub-Category']=='Paper']  # 布尔值索引
data.shape

(1370, 21)

data.head()  # 查看筛选的结果

	Row ID	Order ID	Order Date	Ship Date	Ship Mode	Customer ID	Customer Name	Segment	Country	City	...	Postal Code	Region	Product ID	Category	Sub-Category	Product Name	Sales	Quantity	Discount	Profit
12	13	CA-2018-114412	2018-04-15	2018-04-20	Standard Class	AA-10480	Andrew Allen	Consumer	United States	Concord	...	28027.0	South	OFF-PA-10002365	Office Supplies	Paper	Xerox 1967	15.552	3	0.2	5.4432
34	35	CA-2018-107727	2018-10-19	2018-10-23	Second Class	MA-17560	Matt Abelman	Home Office	United States	Houston	...	77095.0	Central	OFF-PA-10000249	Office Supplies	Paper	Easy-staple paper	29.472	3	0.2	9.9468
56	57	CA-2017-111682	2017-06-17	2017-06-18	First Class	TB-21055	Ted Butterfield	Consumer	United States	Troy	...	12180.0	East	OFF-PA-10001569	Office Supplies	Paper	Xerox 232	32.400	5	0.0	15.5520
58	59	CA-2017-111682	2017-06-17	2017-06-18	First Class	TB-21055	Ted Butterfield	Consumer	United States	Troy	...	12180.0	East	OFF-PA-10000587	Office Supplies	Paper	Array Parchment Paper, Assorted Colors	14.560	2	0.0	6.9888
64	65	CA-2016-135545	2016-11-24	2016-11-30	Standard Class	KM-16720	Kunst Miller	Consumer	United States	Los Angeles	...	90004.0	West	OFF-PA-10003892	Office Supplies	Paper	Xerox 1943	146.730	3	0.0	68.9631

5 rows × 21 columns

# Step3. 绘制散点图
import plotly.graph_objects as go

fig = go.Figure(go.Scatter(
    x=data['Sales'],
    y=data['Profit'],
    mode='markers',
    marker=dict(
        color=data['Discount'],  # 用颜色表示 Discount
        size=12 ),  
    text=data['Discount'],
    opacity=0.7
))

fig.update_layout(
    title='Sales and Profit Distrubution of Paper',
    xaxis=dict(title='Sales'),
    yaxis=dict(title='Profit')
)

fig.show()

# 延伸；用气泡的颜色来展示不同地区（Region）的数据点
fig = go.Figure()

for i in data['Region'].unique():
    fig.add_trace(go.Scatter(
        x = data.loc[data['Region']==i,'Sales'],
        y = data.loc[data['Region']==i,'Profit'],
        mode='markers',
        name=i,
        marker_size=12,
        opacity=0.7
    ))

fig.update_layout(
    title='Sales and Profit Distrubution of Paper by Region',
    xaxis=dict(title='Sales'),
    yaxis=dict(title='Profit')
)

fig.show()

练习2. 绘制气泡图 Sales and Profit Distrubution

导入数据文件'Sample - Superstore.xls'，绘制气泡图，展示销售额最高的前50名客户的销售额（X轴）和利润（Y轴）的关系，气泡的大小size和颜色color均体现折扣（Discount）这一变量，交互时增加显示的文本text：Customer Name和Discount（如交互所示）。

# Step1. 导入数据：导入文件'Sample - Superstore.xls'，并查看数据
import pandas as pd
df = pd.read_excel('Sample - Superstore.xls',sheet_name='Orders')
df.shape

(9994, 21)

# Step2. 数据分析得到销售额最高的前50名客户的销售额（Sales）、利润（Profit）和折扣（Discount）
data = df.groupby('Customer Name').agg({'Sales':'sum','Discount':'mean','Profit':'sum'})
data = data.sort_values(by='Sales',ascending=False)
data = data[:50]
data

	Sales	Discount	Profit
Customer Name
Sean Miller	25043.0500	0.246667	-1980.7393
Tamara Chand	19052.2180	0.116667	8981.3239
Raymond Buch	15117.3390	0.094444	6976.0959
Tom Ashbrook	14595.6200	0.080000	4703.7883
Adrian Barton	14473.5710	0.240000	5444.8055
Ken Lonsdale	14175.2290	0.200000	806.8550
Sanjit Chand	14142.3340	0.063636	5757.4119
Hunter Lopez	12873.2980	0.018182	5622.4292
Sanjit Engle	12209.4380	0.110526	2650.6769
Christopher Conant	12129.0720	0.281818	2177.0493
Todd Sumrall	11891.7510	0.116667	2371.7144
Greg Tran	11820.1200	0.100000	2163.4269
Becky Martin	11789.6300	0.168750	-1659.9581
Seth Vernon	11470.9500	0.156250	1199.4242
Caroline Jumper	11164.9740	0.188500	858.7414
Clay Ludtke	10880.5460	0.114286	1933.7831
Maria Etezadi	10663.7280	0.131818	1859.4695
Karen Ferguson	10604.2660	0.033333	1660.1386
Bill Shonely	10501.6530	0.011111	2616.0644
Edward Hooks	10310.8800	0.071875	1393.5154
John Lee	9799.9230	0.088235	228.9070
Grant Thornton	9351.2120	0.250000	-4108.6589
Helen Wasserman	9300.2540	0.045000	2164.1611
Tom Boeckenhauer	9133.9900	0.070588	2798.3689
Peter Fuller	9062.8640	0.121053	-614.2943
Christopher Martinez	8954.0200	0.120000	3899.8904
Justin Deggeller	8828.0305	0.055882	1619.5199
Joe Elijah	8697.8430	0.322727	1262.2926
Laura Armstrong	8673.2220	0.115385	2059.1199
Pete Kriz	8646.9340	0.076000	2038.2676
Daniel Raglin	8350.8680	0.153846	2869.0760
Natalie Fritzler	8322.8260	0.250000	-1695.9714
Karen Daniels	8282.3580	0.187500	1107.6952
Nick Crebassa	8241.7390	0.136667	1314.7580
Harry Marie	8236.7648	0.231000	2437.9836
Keith Dawkins	8181.2560	0.087500	3038.6254
Sean Braxton	8057.8910	0.241176	-2082.7451
Zuschuss Carroll	8025.7070	0.254839	-1032.1490
Joseph Holt	7954.9980	0.085714	-644.6982
Nora Preis	7903.1825	0.196154	631.2282
Anna Häberlin	7888.2940	0.217391	1298.0166
Adam Bellavance	7755.6200	0.044444	2054.5885
Jim Epp	7754.9760	0.160000	1623.4019
Jane Waco	7721.7140	0.071429	2173.7094
Lena Creighton	7663.1260	0.156522	1288.3469
John Murray	7625.0760	0.184615	1574.6164
Jonathan Doherty	7610.8640	0.075000	1050.2668
Patrick O'Brill	7473.8282	0.210000	38.4757
Maribeth Schnelling	7443.6900	0.160417	844.9355
Rick Wilson	7397.4010	0.134783	1586.6273

# Step3. 绘制气泡图
import plotly.graph_objects as go

text=[]
for i in range(50):
    text.append(('Customer Name: {0}<br>Discount: {1:.2%}').format(data.index[i],data.iloc[i,1]))

fig = go.Figure(go.Scatter(
    x=data['Sales'],
    y=data['Profit'],
    mode='markers',
    marker=dict(
        color=data['Discount'],
        colorscale='viridis',
        showscale=True,
        size=data['Discount'],
        sizeref=2*max(data['Discount'])/(10**2)),
    text=text,
))

fig.update_layout(
    title='Top-50 Customers\' Sales and Profit',
    xaxis_title='Sales',
    yaxis_title='Profit'
)

fig.show()

3.3 线形图 Line（时间序列 Time Series）

Plotly对时间序列的支持比较友好，既支持字符串格式，又支持日期/时间格式。只要传入的参数x是datetime.datetime对象，或者字符串strings，Plotly会自动识别为时间格式。

如果只想展示部分时间范围内的绘图结果，可以在布局layout中通过传递一个range参数即可实现这个功能。如果想要恢复默认的时间范围，可以单击界面右上角的Autoscale按钮。

# 3-6 Time Series 时间序列
import plotly.graph_objects as go
import pandas as pd
import numpy as np
import datetime

# datetime.datetime对象
x0 = [datetime.datetime(year=2020, month=10, day=1),
      datetime.datetime(year=2020, month=10, day=3),
      datetime.datetime(year=2020, month=10, day=5)]
y0 = [0,1,2]

# 字符串对象或array对象
x1 = np.array(['2020-10-01','2020-10-03','2020-10-05'])
y1 = [2,1,0]

# 使用pandas的date_range生成datetime对象
x2 = pd.date_range('20201001',periods=31)
y2 = np.random.randn(31)

fig = go.Figure()

fig.add_trace(go.Scatter(x=x0,y=y0,name='trace_datetime'))
fig.add_trace(go.Scatter(x=x1,y=y1,name='trace_string'))
fig.add_trace(go.Scatter(x=x2,y=y2,name='trace_daterange'))

fig.update_traces(opacity=0.8)

# 使用range参数展示部分时间范围内的绘图结果
fig.update_layout(
    xaxis=dict(range=[x2[0],x2[6]])  # 只显示前7天的绘图结果
    # xaxis_range=[datetime.datetime(2020,10,1),datetime.datetime(2020,10,7)]
)

fig.show()

练习3. 绘制时间序列图 Sales and Profit by day

导入数据文件'Sample - Superstore.xls'，绘制时间序列图，展示2018年每天的销售额（Sales）和利润（Profit）。

# 导入数据并进行数据分析
import pandas as pd
df = pd.read_excel('Sample - Superstore.xls',sheet_name='Orders')

# 每天销售额和利润
data = df.groupby('Order Date')[['Sales','Profit']].sum()
data

	Sales	Profit
Order Date
2015-01-03	16.4480	5.5512
2015-01-04	288.0600	-65.9901
2015-01-05	19.5360	4.8840
2015-01-06	4407.1000	1358.0524
2015-01-07	87.1580	-71.9621
...	...	...
2018-12-26	814.5940	61.1202
2018-12-27	177.6360	-31.9742
2018-12-28	1657.3508	253.1188
2018-12-29	2915.5340	644.4338
2018-12-30	713.7900	101.5365

1236 rows × 2 columns

# 索引和切片
print(data.loc['2018'],'\n')  # 2018年的记录
print(data.loc['2018-02'],'\n')  # 2018年2月的记录
print(data['2018-12-01':'2018-12-15'])  # 切片

                Sales    Profit
Order Date                     
2018-01-01  1481.8280 -181.4109
2018-01-02  2079.5540 -207.0473
2018-01-03  2070.2720  704.2800
2018-01-06    33.7400   15.5204
2018-01-07  3395.5900  758.7192
...               ...       ...
2018-12-26   814.5940   61.1202
2018-12-27   177.6360  -31.9742
2018-12-28  1657.3508  253.1188
2018-12-29  2915.5340  644.4338
2018-12-30   713.7900  101.5365

[322 rows x 2 columns] 

                Sales    Profit
Order Date                     
2018-02-02   913.3540  170.6770
2018-02-03   922.3270  215.5700
2018-02-04    32.6700    8.4942
2018-02-05  2263.0120   74.8820
2018-02-06   904.3540  204.3158
2018-02-09   773.7640 -411.9726
2018-02-10   227.1030   28.1274
2018-02-11  1241.5160  130.1018
2018-02-13  1058.4300  424.3345
2018-02-16  1337.4420   95.9756
2018-02-17  2964.8174 -383.5478
2018-02-18   287.3260   62.4082
2018-02-19  1314.5900  377.0515
2018-02-20  1150.2900 -107.5121
2018-02-21    47.9040   -2.9940
2018-02-23   117.8000   42.3700
2018-02-24  1448.6760  249.3929
2018-02-25   430.4920  -19.3798
2018-02-26  2847.6460  447.3532
2018-02-28    17.6200    8.2242 

               Sales     Profit
Order Date                     
2018-12-01  5331.178   718.8920
2018-12-02  9951.182    -7.3410
2018-12-03  1403.842   280.7407
2018-12-04  2639.638   -21.9881
2018-12-05  1453.136   447.6235
2018-12-06    10.680     2.8836
2018-12-07  2916.514 -2686.6673
2018-12-08  7643.041  1154.6045
2018-12-09  5470.390  1487.1418
2018-12-10  3873.559   715.5696
2018-12-11  2823.965   -82.4089
2018-12-13   580.936    99.2154
2018-12-14  3897.714   215.2500
2018-12-15   306.888    52.5946

# 绘制时间序列图：2018年每天的销售额（Sales）和利润（Profit）
import plotly.graph_objects as go
import pandas as pd

data = df.groupby('Order Date')[['Sales','Profit']].sum()
data = data.loc['2018']

fig = go.Figure()
fig.add_trace(go.Scatter(
    x=data.index,
    y=data['Sales'],
    name='Sales'
))
fig.add_trace(go.Scatter(
    x=data.index,
    y=data['Profit'],
    name='Profit'
))

fig.update_traces(opacity=0.8)

fig.update_layout(
    title='Sales and Profit in 2018'
)

fig.show()

练习4. 绘制时间序列图 Sales and Profit by month

导入数据文件'Sample - Superstore.xls'，绘制时间序列图，展示2018年每个月的销售额（Sales）和利润（Profit）。

对Order Date进行groupby操作后，时间戳是每天（D），如果想要将其转换为每月（M），可以通过重新采样来实现。重新采样是指将时间序列从一个频率转换为另一个频率的过程。将更高频率的数据聚合到低频率被称为向下采样，反之则称为向上采样。Pandas对象配有resample方法，与groupby方法类似，调用resample时需要对数据分组，之后再调用聚合函数。

# 重新采样
data = df.groupby('Order Date')[['Sales','Profit']].sum()
data = data.loc['2018'].resample('M').sum()
data

	Sales	Profit
Order Date
2018-01-31	43971.3740	7140.4391
2018-02-28	20301.1334	1613.8720
2018-03-31	58872.3528	14751.8915
2018-04-30	36521.5361	933.2900
2018-05-31	44261.1102	6342.5828
2018-06-30	52981.7257	8223.3357
2018-07-31	45264.4160	6952.6212
2018-08-31	63120.8880	9040.9557
2018-09-30	87866.6520	10991.5556
2018-10-31	77776.9232	9275.2755
2018-11-30	118447.8250	9690.1037
2018-12-31	83829.3188	8483.3468

data.index.strftime('%Y-%m')  # 转换时间格式

Index(['2018-01', '2018-02', '2018-03', '2018-04', '2018-05', '2018-06',
       '2018-07', '2018-08', '2018-09', '2018-10', '2018-11', '2018-12'],
      dtype='object', name='Order Date')

# 绘制时间序列图：2018年每个月的销售额（Sales）和利润（Profit）

fig = go.Figure()
fig.add_trace(go.Scatter(
    x=data.index.strftime('%Y-%m'),
    y=data['Sales'],
    name='Sales',
    marker_color='#ff7043'
))
fig.add_trace(go.Scatter(
    x=data.index.strftime('%Y-%m'),
    y=data['Profit'],
    name='Profit',
    marker_color='#29b6f6'
))
        
fig.update_layout(
    title='Sales and Profit in 2018',
    xaxis=dict(dtick='M1')  # X轴刻度显示为每一个月
)

fig.show()